I know this is a fairly specific
R question, but I may be thinking about proportion variance explained, $R^2$, incorrectly. Here goes.
I'm trying to use the
randomForest. I have some training data and testing data. When I fit a random forest model, the
randomForest function allows you to input new testing data to test. It then tells you the percentage of variance explained in this new data. When I look at this, I get one number.
When I use the
predict() function to predict the outcome value of the testing data based on the model fit from the training data, and I take the squared correlation coefficient between these values and the actual outcome values for the testing data, I get a different number. These values don't match up.
R code to demonstrate the problem.
# use the built in iris data data(iris) #load the randomForest library library(randomForest) # split the data into training and testing sets index <- 1:nrow(iris) trainindex <- sample(index, trunc(length(index)/2)) trainset <- iris[trainindex, ] testset <- iris[-trainindex, ] # fit a model to the training set (column 1, Sepal.Length, will be the outcome) set.seed(42) model <- randomForest(x=trainset[ ,-1],y=trainset[ ,1]) # predict values for the testing set (the first column is the outcome, leave it out) predicted <- predict(model, testset[ ,-1]) # what's the squared correlation coefficient between predicted and actual values? cor(predicted, testset[, 1])^2 # now, refit the model using built-in x.test and y.test set.seed(42) randomForest(x=trainset[ ,-1], y=trainset[ ,1], xtest=testset[ ,-1], ytest=testset[ ,1])
Thanks for any help you might be willing to lend.